The rapid rise of e-commerce apps has increased the accumulation of data. To forecast outcomes, data mining, also known as KDD (Knowledge Discovery in Databases), is used to detect irregularities, linkages, trends and patterns in data.

An algorithm known as Apriori is a common one in data mining. It's used to identify the most frequently occurring elements and meaningful associations in a dataset. As an example, products brought in by consumers to a shop may all be used as inputs in this system.

An effective Market Basket Analysis is critical since it allows consumers to purchase their products with more convenience, resulting in a rise in market sales. Furthermore, it has been applied in healthcare to aid in the identification of harmful medication responses. A clustering algorithm is generated that identifies which combinations of drugs and patient factors are associated with adverse drug reactions.

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Apriori Property

In 1994, R. Agrawal and R. Srikant developed the Apriori method for identifying the most frequently occurring itemsets in a dataset using the boolean association rule. Since it makes use of previous knowledge about common itemset features, the method is referred to as Apriori. This is achieved by the use of an iterative technique or level-wise approach, in which k-frequent itemsets are utilized to locate k+1 itemsets.

An essential feature known as the Apriori property is utilized to boost the effectiveness of level-wise production of frequent itemsets. This property helps by minimizing the search area, which in turn serves to maximize the productivity of level-wise creation of frequent patterns.

How Does the Apriori Algorithm Work?

The Apriori algorithm operates on a straightforward premise. When the support value of an item set exceeds a certain threshold, it is considered a frequent item set. Take into account the following steps. To begin, set the support criterion, meaning that only those things that have more than the support criterion are considered relevant. 

  • Step 1: Create a list of all the elements that appear in every transaction and create a frequency table.
  • Step 2: Set the minimum level of support. Only those elements whose support exceeds or equals the threshold support are significant. 
  • Step 3: All potential pairings of important elements must be made, bearing in mind that AB and BA are interchangeable. 
  • Step 4: Tally the number of times each pair appears in a transaction.
  • Step 5: Only those sets of data that meet the criterion of support are significant. 
  • Step 6: Now, suppose you want to find a set of three things that may be bought together. A rule, known as self-join, is needed to build a three-item set. The item pairings OP, OB, PB, and PM state that two combinations with the same initial letter are sought from these sets.
  1. OPB is the result of OP and OB.
  2. PBM  is the result of PB and PM.
  • Step 7: When the threshold criterion is applied again, you'll get the significant itemset.  

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Steps for Apriori Algorithm

The Apriori algorithm has the following steps:

  • Step 1: Determine the level of transactional database support and establish the minimal degree of assistance and dependability.
  • Step 2: Take all of the transaction's supports that are greater than the standard or chosen support value. 
  • Step 3: Look for all rules with greater precision than the cutoff or baseline standard, in these subgroups.
  • Step 4: It is best to arrange the rules in ascending order of strength.

Methods to Improve Apriori Efficiency

The algorithm's efficiency may be improved in a variety of ways.

  • Hash-Based Technique

Using a hash-based structure known as a hash table, the k-itemsets and their related counts are generated. The table is generated using a hash function.

  • Transaction Reduction

There are fewer transactions to scan throughout each loop when using this strategy. Items that are not often used in a process are either tagged or deleted.

  • Partitioning

Two database searches are all that is needed to find the frequently occurring itemsets using this approach. For any item set to be considered "possibly frequent" in the database, it must be prevalent in at least a few of the database subdivisions.

  • Sampling

A random sample S is selected from database D, and then a search is conducted for frequent itemsets within that sample S. Global frequent itemsets may be misplaced. By reducing the min sup, this may be decreased. 

  • Dynamic Itemset Counting

During the screening of the dataset, this approach may add new iterations at any indicated starting position of the directory. 

Advantages of Apriori

  •  An algorithm that is simple to grasp.
  •  The Merge and Squash processes are simple to apply on big itemsets in huge databases. 

Disadvantages of Apriori

  • It requires a significant amount of calculations if the itemsets are extremely big and the minimal support is maintained to a bare minimum.
  • A full scan of the whole database is required.

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Applications of Apriori Algorithm

Apriori is used in the following fields:

  • Education 

Through the use of traits and specializations, data mining of accepted students may be used to extract association rules. 

  • Medical 

Analyzing the patient's database, for example, might be appropriate.

  • Forestry 

Frequency and intensity of forest fire analysis using forest fire data.

  • Autocomplete Tool 

Apriori is employed by a number of firms, including Amazon's recommender system and Google's autocomplete tool.

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There will be a 42.8 per cent CAGR in the Machine Learning sector by 2024, reflecting a growing acceptance of the technology by businesses. Clamour for Machine Learning experts is predicted to increase by 11% by 2024.

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